Solutions

Master Data Management

Control your data - control your business!

Master Data Management (MDM) combines all the organizational and/or technology-based activities for the sustained improvement of the enterprise-wide master data. These take place in cooperation between various business units and the IT department. The improvement of the data ideally occurs, when entering the data.

Biggest challenges for companies in master data management:

Master Data Management provides the following advantages for your business:

Identification of “Golden Records”.
Creating a 360-degree view of the data

The integration of data from various IT systems ( CRM , accounting, or EXCEL ) allows a consistent view of the data . Incomplete , duplicate and inconsistent data sets are unified and displayed centrally . Find out if Peter Müller and Peter Mueller are the same person.

Illustration of the relationships between data from different sources

Data from various IT systems are interdependent. Through rules ( mapping ) these dependencies are clear. So you can find out, how much revenue your company has made with the “right” Peter Müller.

Control over business processes

Master data are used and processed by different people and different departments. Keep track of the changes in the customer data, associated product or payment functions and manage which individuals are allowed to make data changes. You can configure that e.g. only the account manager of Mr. Müller is allowed to change his master data.

Data quality is affected by the way data is entered, saved and managed. The verification process of reliability and effectivity of data is referred to as data quality management.
Preserving data quality implies checking and cleaning databases regularly.

The benefits that you can achieve in your business through high quality data are highly dependent on the available data and relevant business processes and therefore always individual. Hence we recommend an initial screening of the data landscape at the beginning of a master data project. Typical advantages that are achieved by high data quality , are the following:

Optimized, accurate and fast processes

Increased customer and personnel satisfaction

Better evaluations and reports

Optimized Warehouse and Order Management

Simplified implementation of new systems, new standards or organizational techniques (e.g. ITIL)

DataCanvas

On which lever is your master data management? With the help of DataCanvas you will easily find out and identify potentials to optimize your master data management.

The DataCanvas is a strategic management and entrepreneurial tool. It allows Enterprises to understand, structure, categorize, evaluate, control and optimize their Master Data Management.

The DataCanvas is composed of four major Elements: (1) Objectives; (2) Building Blocks; (3) Actions; (4) Maturity Modell (MDM3).

The whole DataCanvas is run through iteratively and discussed in the group. Using the DataCanvas you can systematically structure your running master data Management and position your current MDM-Activities in the Maturity Modell (MDM3).

We will be happy to presemt you our DataCanvas free of charge and without any obligations or run it through with your team in a workshop at your office or site.

Record of results with recommendations for action and positioning in maturity model MDM3

Datamigration and -consolidation

Data migrations and consolidations are tedious - and we're there to help you!

Modernization projects, acquisitions, mergers or consolidation projects have a direct impact on your system landscape. For example the application portfolio changes and data must be moved from System A to System B – in short: you are facing a typical migration project. It’s irrelevant whether your data is in the cloud or on your servers and if it’s a customer- or mainframe system.

A data migration is the process of data being transferred in between storage systems, data formats, or computer systems.

Data migrations are one of the most common problems of poor data quality. Poor data quality leads to delayed migration projects and overspending of your budget.

Data Rocket supports the three key steps of a data migration (extraction, transformation and loading). During the migration of data from one system to another the data quality can be improved – the big advantage is that thereby only “clean” data is migrated. The data is transferred via batch import or bidirectional interface to the source data systems.

Total Quality Management

MDM and DQ-Projects are about team work!

Total Quality Management ( TQM ) is a management approach to optimize the quality of products and services of a company in all functional areas and at all levels by participation of all employees. DataRocket transfers this proven approach to improve data quality and Master Data Management.

Open Data Enrichment

To be able to optimize plausibility controls and data validation, you need additional data.
External data sources e.g. professional data services or publicly available data sources are used to carry out data validations or plausibility checks (checks for logic and determination of plausibility).

…gain new information about customers (Data Mining)
(e.g. at what point is the willingness to buy particularly high)

Through the enrichment of your data you can significantly improve your data quality. You can quickly identify new customers, contact them more specifically and thereby increase your conversion rate.

DataRocket offers two options for enriching your data

1. Use of Open Data

Open Data are freely available digital web data that may be used free of cost without legal restrictions. These are for example information from Open Street Map or Wikipedia, but also data from social media such as XING, Facebook or G+.

Data Rocket has interfaces to web services and can search for specific data, which are then used to improve your data quality or to optimize your sales process.

2. Use of professional data services

Existing data dispatchers (e.g. German Post Direkt, Universal Postal Service, Creditreform) are used. The data is acquired from the service providers and gets transferred to Data Rocket for adjustment and integration into the data base.